1. Field of the Invention
The invention relates to image recognition through a comparison of stored images as compared to an image to be matched, and more specifically, to facial recognition whereby sub-regions of images are compared and/or normalized to account for lighting, expressions, and/or other conditions so as to improve the accuracy of the comparison.
2. Description of the Related Art
Facial recognition is a popular topic in biometric applications. Specifically, face recognition, and especially automatic facial recognition, is of interest as compared with iris or fingerprint recognition technologies. Such face recognition technology is of particular interest for security purposes. For instance, automatic facial recognition has been selected as an essential part of new versions of passports by many countries for implementation in one or two years. Additionally, facial recognition technology is also generally recognized as useful in, among other areas, crime prevention, national security, and private security purposes. Furthermore, face recognition is a worthy research topic and has promoted the development of pattern recognition and computer vision.
A problem with conventional automatic facial recognition technologies is that there is a greater need for an ordinary inspector (i.e., a user) to assist in the facial recognition since the existing technologies often fail. Specifically, in order to perform face recognition, the technology needs to account for the facial texture of the face, the 3D geometry of the face, the fact that the face is non-rigid and is thus capable of various expressions, any occlusions or blocking of features such as occurs with glasses or hair, and a complex illumination environment. These factors make face recognition a difficult problem.
Several studies have been reported in recent years that compare and evaluate the conventional face recognition algorithms and technology. Two such studies are published in D. Blackburn, M. Bone, and P. Phillips. Facial Recognition Vendor Test 2000: Evaluation Report, 2000, and in P. J. Phillips, H. Moon, S. Rizvi, and P. Rauss, The FERET Evaluation Methodology for Face Recognition Algorithms: IEEE Trans. On PAMI, 22(10): 1090-1103, (2000). These studies show that current algorithms are not robust against changes in facial expression, illumination, pose and occlusion.
Additionally, in performing face recognition, it is important that the feature selection be properly performed. If a good feature is selected, the classification would be a relatively easy task. For instance, with good feature selection, even simple classification techniques such as K-mean clustering or KNN processes based on Euclidian distance will work well. However, this method is dependent on an assumption that in the suitable feature subspace, the samples in the same class are Gaussian distributed and there is less overlap between different classes. However, while there has been a great deal of work to try and apply this method for face recognition, a suitable feature subspace needed to perform this method has not been found. For instance, no suitable feature subspace has been found in using Principal Components Analysis (PCA), Linear Discriminant Analysis (LDA), or Locality Preserving Projections (LPP) for face representation and feature selection. Generally, the PCA analysis seeks a projection that best represents the data in the least square sense, the LDA analysis seeks a projection that best separates the data in a least square sense, and LPP finds an embedding that preserves the local information, and obtains a face space that best detects the essential manifold structure.
Descriptions of the PCA, LDA, and LPP methods are described in M. Turk and Pentland, Face Recognition Using Eigenfaces (IEEE 1991), P. N. Belhumeur, J. P. Hespanda and D. J. Kriegman, Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Projection, IEEE Trans PAMI, vol.19, No. 7, pp.711-720 (1997), and Xiaofei He, Shuicheng Yan, Yuxiao Hu, Hong-Jiang Zhang, Learning a Locality Preserving Subspace for Visual Recognition, Proceedings of the Ninth IEEE International Conference on Computer Vision, Pages 385-392 (ICCV 2003), the disclosures of which are incorporated by reference.
One of the reasons for the difficulty of face recognition feature selection is that face images reside on a nonlinear manifold (i.e., in a surface or space which is nonlinear). Due to the complex face manifold, the traditional Euclidian distance (i.e., a straight line distance between two points) used to determine a correspondence between images will not work for a face recognition task. To solve this problem a Geodesic distance (i.e., a shortest distance between two points, linear or non-linear) using ISOMAP was introduced to solve this problem. A more detailed description is found in Joshua B Tenenbaum, Vin de Silva, and John C. Langford, A Global Geometric Framework for Nonlinear Dimensional Reduction, Science, vol 290 (Dec. 22, 2000). However, some researchers have found that, in order to make ISOMAP work in practical usage, the parameter space needs to be decomposed into a series of overlapping convex pieces. As such, the difficulty of the manifold approach is that practical usage cannot provide abundant samples to describe a personal specific manifold such that the manifold approach still has a long way to go before practical usage is achieved.